SoroushVahidi/scidocs huggingface dataset:
Malware detection using adaptive data compression
(2008)

A popular approach in current commercial anti-malware software detects malicious programs by searching in the code of programs for scan strings that are byte sequences indicative of malicious code. The scan strings, also known as the signatures of existing malware, are extracted by malware analysts from known malware samples, and stored in a database often referred to as a virus dictionary. This process often involves a significant amount of human efforts. In addition, there are two major limitations in this technique. First, not all malicious programs have bit patterns that are evidence of their malicious nature. Therefore, some malware is not recorded in the virus dictionary and can not be detected through signature matching. Second, searching for specific bit patterns will not work on malware that can take many forms--obfuscated malware. Signature matching has been shown to be incapable of identifying new malware patterns and fails to recognize obfuscated malware. This paper presents a malware detection technique that discovers malware by means of a learning engine trained on a set of malware instances and a set of benign code instances. The learning engine uses an adaptive data compression model--prediction by partial matching (PPM)--to build two compression models, one from the malware instances and the other from the benign code instances. A code instance is classified, either as "malware" or "benign", by minimizing its estimated cross entropy. Our preliminary results are very promising. We achieved about 0.94 true positive rate with as low as 0.016 false positive rate. Our experiments also demonstrate that this technique can effectively detect unknown and obfuscated malware.
An updated survey of GA-based multiobjective optimization techniques
(2000)

After using evolutionary techniques for single-objective optimization during more than two decades, the incorporation of more than one objective in the fitness function has finally become a popular area of research. As a consequence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The purpose of this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the operations research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new ways of exploiting the search capabilities of evolutionary algorithms. Furthermore, a summary of the main algorithms behind these approaches is provided, together with a brief criticism that includes their advantages and disadvantages, degree of applicability, and some known applications. Finally, further trends in this area and some possible paths for further research are also addressed.
Understanding PubMed® user search behavior through log analysis
(2009)

This article reports on a detailed investigation of PubMed users' needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users' needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users' interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users' decisions. Analysis of characteristics such as these plays a critical role in identifying users' information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval.Database URL:http://www.ncbi.nlm.nih.gov/PubMed.
Pattern classification of foot strike type using body worn accelerometers
(2013)

The automatic classification of foot strike patterns into the three basic categories forefoot, midfoot and rearfoot striking plays an important role for applications like shoe fitting with instant feedback. This paper presents methods for this classification based on body worn accelerometers that allow giving the required direct feedback to the user. For our study, we collected data from 40 runners who had a standard accelerometer in a custom-built sensor pod attached to the laces of their running shoes. The acceleration in three axes was recorded continuously while the runners conducted their runs. Data for repeated runs at two different speed levels were collected in order to have sufficient sensor data for classification. The data was analyzed using features computed for individual steps of the runners to distinguish the three foot strike pattern classes. The labels for the strike pattern classes were established using high-speed video that was concurrently collected. We could show that the classification of the strike types based on the measured accelerations and the extracted features was up to 95.3% accurate. The established classification system can be used to support runners, for example by giving running shoe recommendations that ideally match the prevailing strike type of the runner.
SUPPRESSION OF GATE INDUCED DRAIN LEAKAGE CURRENT ( GIDL ) BY GATE WORKFUNCTION ENGINEERING : ANALYSIS AND MODEL
(2012)

Leakage current reduction is of primary importance as the technology scaling trends continue towards deep sub-micrometer regime. One of the leakage mechanisms which contribute significantly to power dissipation is the Gate Induced Drain Leakage (GIDL). GIDL sets an upper limit on the VLSI MOSFET scaling and may even lead to device breakdown. Thus, in order to improve performance, static power consumption becomes a major concern in such miniaturized devices. With the CMOS technology in the nanometer regime, metal gates emerge as a powerful booster over the poly-Si gates, keeping the leakage mechanisms in control. This work presents a systematic study of the Gate Induced Drain Leakage current reduction by changing the gate workfunction. In this work, an attempt has been made to model the metal gates in the field equations in the gate-drain overlap region. The modeling has been accomplished by taking the physical property of the metals into account i.e. the workfunction. The analytical model has been used to study the impact of gate workfunction engineering on GIDL. The model includes the calculation of band bending, the resultant electric field in the gate-drain overlap region and the GIDL current. It has been observed that the electric field and the GIDL current decreases as the gate workfunction is increased continuously from 4.0~5.2 eV. Further, the results are more interesting at low applied voltages where the decrease in the current is of about 6 orders of magnitude as compared to that at higher voltages.
On the Interpolation of Data with Normally Distributed Uncertainty for Visualization
(2012)

In many fields of science or engineering, we are confronted with uncertain data. For that reason, the visualization of uncertainty received a lot of attention, especially in recent years. In the majority of cases, Gaussian distributions are used to describe uncertain behavior, because they are able to model many phenomena encountered in science. Therefore, in most applications uncertain data is (or is assumed to be) Gaussian distributed. If such uncertain data is given on fixed positions, the question of interpolation arises for many visualization approaches. In this paper, we analyze the effects of the usual linear interpolation schemes for visualization of Gaussian distributed data. In addition, we demonstrate that methods known in geostatistics and machine learning have favorable properties for visualization purposes in this case.
Two-factor authentication for the Bitcoin protocol
(2016)

We show how to realize two-factor authentication for a Bitcoin wallet. To do so, we explain how to employ an ECDSA adaption of the two-party signature protocol by MacKenzie and Reiter (Int J Inf Secur 2(3–4):218–239, 2004. doi: 10.1007/s10207-004-0041-0 ) in the context of Bitcoin and present a prototypic implementation of a Bitcoin wallet that offers both: two-factor authentication and verification over a separate channel. Since we use a smart phone as the second authentication factor, our solution can be used with hardware already available to most users and the user experience is quite similar to the existing online banking authentication methods.
Jointly Detecting and Separating Singing Voice: A Multi-Task Approach
(2018)

A main challenge in applying deep learning to music processing is the availability of training data. One potential solution is Multi-task Learning, in which the model also learns to solve related auxiliary tasks on additional datasets to exploit their correlation. While intuitive in principle, it can be challenging to identify related tasks and construct the model to optimally share information between tasks. In this paper, we explore vocal activity detection as an additional task to stabilise and improve the performance of vocal separation. Further, we identify problematic biases specific to each dataset that could limit the generalisation capability of separation and detection models, to which our proposed approach is robust. Experiments show improved performance in separation as well as vocal detection compared to single-task baselines. However, we find that the commonly used Signal-to-Distortion Ratio (SDR) metrics did not capture the improvement on non-vocal sections, indicating the need for improved evaluation methodologies.
Authenticity and Subjective Wellbeing within the Context of a Religious Organization
(2017)

Although authenticity has a long history as a philosophical and psychological idea, this concept has received scarce attention in the business literature until very lately. Nevertheless, scholars belonging to a broad array of disciplines have pointed out the escalation in the individuals' search for authenticity within developed societies. Hence, the purpose of this paper is to assess the link between authenticity and subjective wellbeing within the rarely explored context of faith-driven organizations, where the management of emotions attains a particular significance. Specifically, this study links authenticity with subjective wellbeing among the distinct groups that shape a large international Catholic organization. This study uses Partial Least Squares (PLS) to test our research model and hypotheses. This paper covers two noteworthy research gaps. On the one hand, it provides evidence of the relationship between authenticity and subjective wellbeing within the context of religious organizations. On the other hand, our results suggest that this relationship is not homogeneous among the distinct groups that shape the organization. Implications of the research are finally discussed.
Data-Driven Feature Characterization Techniques for Laser Printer Attribution
(2017)

Laser printer attribution is an increasing problem with several applications, such as pointing out the ownership of crime proofs and authentication of printed documents. However, as commonly proposed methods for this task are based on custom-tailored features, they are limited by modeling assumptions about printing artifacts. In this paper, we explore solutions able to learn discriminant-printing patterns directly from the available data during an investigation, without any further feature engineering, proposing the first approach based on deep learning to laser printer attribution. This allows us to avoid any prior assumption about printing artifacts that characterize each printer, thus highlighting almost invisible and difficult printer footprints generated during the printing process. The proposed approach merges, in a synergistic fashion, convolutional neural networks (CNNs) applied on multiple representations of multiple data. Multiple representations, generated through different pre-processing operations, enable the use of the small and lightweight CNNs whilst the use of multiple data enable the use of aggregation procedures to better determine the provenance of a document. Experimental results show that the proposed method is robust to noisy data and outperforms existing counterparts in the literature for this problem.
A Low-delay Protocol for Multihop Wireless Body Area Networks
(2007)

Wireless body area networks (WBANs) form a new and interesting area in the world of remote health monitoring. An important concern in such networks is the communication between the sensors. This communication needs to be energy efficient and highly reliable while keeping delays low. Mobility also has to be supported as the nodes are positioned on different parts of the body that move with regard to each other. In this paper, we present a new cross-layer communication protocol for WBANs: CICADA or Cascading Information retrieval by Controlling Access with Distributed slot Assignment. The protocol sets up a network tree in a distributed manner. This tree structure is subsequently used to guarantee collision free access to the medium and to route data towards the sink. The paper analyzes CICADA and shows simulation results. The protocol offers low delay and good resilience to mobility. The energy usage is low as the nodes can sleep in slots where they are not transmitting or receiving.
The Role of Smart Meters in Enabling Real-Time Energy Services for Households : The Italian Case
(2017)

The Smart Meter (SM) is an essential tool for successful balancing the demand-offer energy curve. It allows the linking of the consumption and production measurements with the time information and the customer’s identity, enabling the substitution of flat-price billing with smarter solutions, such as Time-of-Use or Real-Time Pricing. In addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements.
Ichthyosis congenita, harlequin fetus type: a case report.
(2009)

Ichthyosis is a very heterogeneous family of skin disorders with harlequin ichthyosis being the most severe genetic form. It is a rare autosomal recessive condition, characterized by dry, severely thickened skin with large plates of hyperkeratotic scale, separated by deep fissures. Infants are very susceptible to metabolic abnormalities and infections. They usually do not survive for very long, but several long term survivals have been noted. The vast majority of affected individuals are homozygous for mutations in the ABCA12 gene, which cause a deficiency of the epidermal lipid transporter, resulting in hyperkeratosis and abnormal barrier function. We report a case of a newborn with harlequin ichthyosis, born to unrelated parents, who had a favorable evolution with topical treatment and intensive care.
Classification accuracy is not enough
(2013)

We argue that an evaluation of system behavior at the level of the music is required to usefully address the fundamental problems of music genre recognition (MGR), and indeed other tasks of music information retrieval, such as autotagging. A recent review of works in MGR since 1995 shows that most (82 %) measure the capacity of a system to recognize genre by its classification accuracy. After reviewing evaluation in MGR, we show that neither classification accuracy, nor recall and precision, nor confusion tables, necessarily reflect the capacity of a system to recognize genre in musical signals. Hence, such figures of merit cannot be used to reliably rank, promote or discount the genre recognition performance of MGR systems if genre recognition (rather than identification by irrelevant confounding factors) is the objective. This motivates the development of a richer experimental toolbox for evaluating any system designed to intelligently extract information from music signals.
A game-theoretic method of fair resource allocation for cloud computing services
(2009)

As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.
Effect of subjective perspective taking during simulation of action: a PET investigation of agency
(2001)

Perspective taking is an essential component in the mechanisms that account for intersubjectivity and agency. Mental simulation of action can be used as a natural protocol to explore the cognitive and neural processing involved in agency. Here we took PET measurements while subjects simulated actions with either a first-person or a third-person perspective. Both conditions were associated with common activation in the SMA, the precentral gyrus, the precuneus and the MT/V5 complex. When compared to the first-person perspective, the third-person perspective recruited right inferior parietal, precuneus, posterior cingulate and frontopolar cortex. The opposite contrast revealed activation in left inferior parietal and somatosensory cortex. We suggest that the right inferior parietal, precuneus and somatosensory cortex are specifically involved in distinguishing self-produced actions from those generated by others.
Agile Software Development Methods: Review and Analysis
(2002)

Agile – denoting “the quality of being agile; readiness for motion; nimbleness, activity, dexterity in motion” – software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked a substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being ”agile” against the defined criteria. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.
A Framework for Clustering Uncertain Data Streams
(2008)

In recent years, uncertain data management applications have grown in importance because of the large number of hardware applications which measure data approximately. For example, sensors are typically expected to have considerable noise in their readings because of inaccuracies in data retrieval, transmission, and power failures. In many cases, the estimated error of the underlying data stream is available. This information is very useful for the mining process, since it can be used in order to improve the quality of the underlying results. In this paper we will propose a method for clustering uncertain data streams. We use a very general model of the uncertainty in which we assume that only a few statistical measures of the uncertainty are available. We will show that the use of even modest uncertainty information during the mining process is sufficient to greatly improve the quality of the underlying results. We show that our approach is more effective than a purely deterministic method such as the CluStream approach. We will test the approach on a variety of real and synthetic data sets and illustrate the advantages of the method in terms of effectiveness and efficiency.
Weakly Supervised Affordance Detection
(2017)

Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications. In this work, we introduce a pixel-wise annotated affordance dataset of 3090 images containing 9916 object instances. Since parts of an object can have multiple affordances, we address this by a convolutional neural network for multilabel affordance segmentation. We also propose an approach to train the network from very few keypoint annotations. Our approach achieves a higher affordance detection accuracy than other weakly supervised methods that also rely on keypoint annotations or image annotations as weak supervision.